#' @title Species Abundance Changes
#'
#' @description Calculates the abundance change for species in a replicate
#' between two time points. Changes are on abundance values provided, if
#' relative data is used, then changes in relative abundance will be
#' calculated.
#'
#' @inheritParams RAC_change
#'
#' @return The abundance_change function returns a data frame with a subset of
#' the following columns:
#' \itemize{
#' \item{replicate.var: }{A column with the specified replicate.var, if it is
#' specified.}
#' \item{time.var: }{A column with the specified time.var and a second column,
#' with '2' appended to the name. Time is subtracted from time2}
#' \item{species.var: }{A column with the specified species.var.}
#' \item{change: }{A numeric column of the change in abundance between time
#' points. A positive value occurs when a species increases in abundance over
#' time, and a negative value when a species decreases in abundance over time.}
#' }
#' @references Avolio et al. Submitted
#' @examples
#' data(pplots)
#' # Without replicates
#' df <- subset(pplots, plot == 25)
#' abundance_change(df = df,
#' species.var = "species",
#' abundance.var = "relative_cover",
#' time.var = "year")
#'
#' # With replicates
#' df <- subset(pplots, year < 2004 & plot %in% c(6, 25, 32))
#' abundance_change(df = df,
#' species.var = "species",
#' abundance.var = "relative_cover",
#' replicate.var = "plot",
#' time.var = "year")
#'
#' # With reference year
#' df <- subset(pplots, year < 2005 & plot %in% c(6, 25, 32))
#' abundance_change(df = df,
#' species.var = "species",
#' abundance.var = "relative_cover",
#' replicate.var = "plot",
#' time.var = "year",
#' reference.time = 2002)
#' @export
abundance_change <- function(df,
time.var,
species.var,
abundance.var,
replicate.var = NULL,
reference.time = NULL) {
# validate function call and purge extraneous columns
args <- as.list(match.call()[-1])
df <- do.call(check_args, args, envir = parent.frame())
# add zeros for species absent from a time period within a replicate
by <- c(replicate.var)
allsp <- split_apply_combine(df, by, FUN = fill_species, species.var, abundance.var)
# merge subsets on time difference of one time step
cross.var <- time.var
cross.var2 <- paste(cross.var, 2, sep = '')
split_by <- c(replicate.var)
merge_to <- !(names(allsp) %in% split_by)
if (is.null(reference.time)) {
ranktog <- split_apply_combine(allsp, split_by, FUN = function(x) {
y <- x[merge_to]
cross <- merge(x, y, by = species.var, suffixes = c('', '2'))
f <- factor(cross[[cross.var]])
f2 <- factor(cross[[cross.var2]], levels = levels(f))
idx <- (as.integer(f2) - as.integer(f)) == 1
cross[idx, ]
})
} else {
ranktog <- split_apply_combine(allsp, split_by, FUN = function(x) {
y <- x[x[[time.var]] != reference.time, merge_to]
x <- x[x[[time.var]] == reference.time, ]
merge(x, y, by = species.var, suffixes = c('', '2'))
})
}
# remove rows with NA for both abundances (preferably only when introduced
# by fill_species)
idx <- is.na(ranktog[[abundance.var]])
abundance.var2 <- paste(abundance.var, 2, sep = '')
idx2 <- is.na(ranktog[[abundance.var2]])
ranktog[idx, abundance.var] <- 0
ranktog[idx2, abundance.var2] <- 0
idx <- ranktog[[abundance.var]] != 0 | ranktog[[abundance.var2]] != 0
output <- ranktog[idx, ]
# append change column
output[['change']] <- output[[abundance.var2]] - output[[abundance.var]]
output_order <- c(
time.var, paste(time.var, '2', sep = ''),
replicate.var,
species.var,
'change')
return(output[intersect(output_order, names(output))])
}
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